---
title: granite-docling-258M-mlx
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/granite-docling-258M-mlx"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/granite-docling-258M-mlx.md"
repository: AI-ModelScope/granite-docling-258M-mlx
last_updated: 2025-09-17
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - idefics3
architectures:
  - Idefics3ForConditionalGeneration
base_model:
  - ibm-granite/granite-docling-258M
base_model_relation: finetune
parameters: 315.3M
tensor_type:
  - BF16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task vlm/text/emb"
  - "vllm 0.9.2"
downloads: 125
stars: 1
tags:
  - mlx
---

# granite-docling-258M-mlx

> granite-docling-258M-mlx - AI-ModelScope 在 ModelScope 开源的模型。granite-docling-258M-mlx

AI-ModelScope/granite-docling-258M-mlx 是 ModelScope 魔搭社区上的 315.3M 参数image-text-to-text模型，采用 apache-2.0 许可，基于 ibm-granite/granite-docling-258M 构建，可用 deploy_task vlm/text/emb、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/granite-docling-258M-mlx
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 315.3M
- **Base model**: ibm-granite/granite-docling-258M
- **Inference backends**: deploy_task vlm/text/emb, vllm 0.9.2
- **Tags**: mlx
- **Downloads**: 125
- **Stars**: 1
- **Last updated**: 2025-09-17

Source: https://www.modelscope.cn/models/AI-ModelScope/granite-docling-258M-mlx

---

# granite-docling-258M-mlx

<div style="display: flex; align-items: center;">
    <img src="https://huggingface.co/ibm-granite/granite-docling-258M/resolve/main/granite_docling.png" alt="Granite Docling Logo" style="width: 200px; height: auto; margin-right: 20px;">
    <div>
        <p>Granite Docling is a multimodal Image-Text-to-Text model engineered for efficient document conversion. It preserves the core features of Docling while maintaining seamless integration with <a href="https://docling-project.github.io/docling ">DoclingDocuments</a> to ensure full compatibility. </p>
    </div>
</div>


This model was converted to MLX format from [`ibm-granite/granite-docling-258M`](https://huggingface.co/ibm-granite/granite-docling-258M) using mlx-vlm version **0.3.3**.
Refer to the [original model card](https://huggingface.co/ibm-granite/granite-docling-258M) for more details on the model.

💡 This MLX model is optimized to run efficiently on Apple Silicon Macs.

## How to use this model with Docling

If you run through [🐥Docling](https://github.com/docling-project/docling), it will automatically choose the MLX version of the Granite-Docling model. 
You can select it with the CLI options shown below:

```sh
# Convert to HTML and Markdown:
docling --to html --to md --pipeline vlm --vlm-model granite_docling "https://arxiv.org/pdf/2501.17887" # accepts files, urls or directories

# Convert to HTML including layout visualization:
docling --to html_split_page --show-layout --pipeline vlm --vlm-model granite_docling "https://arxiv.org/pdf/2501.17887"
```

<p align="center">
<img src="https://huggingface.co/ibm-granite/granite-docling-258M/resolve/main/assets/granite_docling_split_page.png" alt="GraniteDocling result in split page view" width="900"/>
</p>

## How to use this model with bare mlx-vlm

You can also run plain mlx-vlm to generate predictions.

To run with the `mlx-vlm` CLI, use this command:

```sh
pip install mlx_vlm 
python -m mlx_vlm.generate --model ibm-granite/granite-docling-258M-mlx --max-tokens 4096 --temperature 0.0 --prompt "Convert this page to docling." --image <path_to_image>
```

To run with the `mlx-vlm` python SDK, parse the output as a `DoclingDocument` and export to various formats (e.g. Markdown, HTML), please refer to the code below.

```python
# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "docling-core",
#     "mlx-vlm", 
#     "pillow",
#     "transformers",
# ]
# ///

import webbrowser
from pathlib import Path

from docling_core.types.doc import ImageRefMode
from docling_core.types.doc.document import DocTagsDocument, DoclingDocument
from mlx_vlm import load, stream_generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
from transformers.image_utils import load_image

# Configuration
MODEL_PATH = "ibm-granite/granite-docling-258M-mlx"
PROMPT = "Convert this page to docling."
SHOW_IN_BROWSER = True

# Sample images (pick one...)
# SAMPLE_IMAGE = "https://huggingface.co/ibm-granite/granite-docling-258M/resolve/main/assets/new_arxiv.png"
# SAMPLE_IMAGE = "https://ibm.biz/docling-page-with-list"
SAMPLE_IMAGE = "https://ibm.biz/docling-page-with-table"

# Load model and processor
print("Loading model...")
model, processor = load(MODEL_PATH)
config = load_config(MODEL_PATH)

# Prepare input image and prompt
print("Preparing input...")
pil_image = load_image(SAMPLE_IMAGE)
formatted_prompt = apply_chat_template(processor, config, PROMPT, num_images=1)

# Generate DocTags output
print("Generating DocTags...\n")
output = ""
for token in stream_generate(
    model, processor, formatted_prompt, [pil_image], max_tokens=4096, verbose=False
):
    output += token.text
    print(token.text, end="")
    if "</doctag>" in token.text:
        break

print("\n\nProcessing output...")

# Create DoclingDocument from generated DocTags
doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([output], [pil_image])
doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Sample Document")

# Export to different formats
print("\nMarkdown output:\n")
print(doc.export_to_markdown())

# Save as HTML with embedded images
output_path = Path("./output.html") 
doc.save_as_html(output_path, image_mode=ImageRefMode.EMBEDDED)
print(f"\nHTML saved to: {output_path}")

# Open in browser
if SHOW_IN_BROWSER:
    webbrowser.open(f"file:///{str(output_path.resolve())}")    
```